Chest radiography is a widely used technique in diagnostic imaging of medical images. It can make up at least a third of all conventional diagnostic radiographic procedures in a general hospital. A conventional radiographic chest examination includes two projection views. One view is a posterior-anterior (PA) or anterior-posterior (AP) projection view, while the other view is a lateral (LAT) projection view.
Medical professionals using such radiographic chest examinations often prefer to view the images in standardized ways, which require the proper position of the image with a particular view (i.e., AP/PA or LAT) relative to the other and the correct orientation of each. However, some x-ray exposures are acquired before they are actually processed to produce visible images. Both film-screen and computed radiography (CR) methods are examples of where x-ray exposures are acquired before they are actually processed to produce visible images. As such, a film or a CR cassette can be exposed for either of a LAT or PA/AP view, and the orientation of the image with respect to the patient may vary for each exposure to accommodate the examination conditions. Accordingly, these exposures/images are often digitized with the view and orientation unknown or mislabeled.
Hence, a method for automatically recognizing the projection view and the patient-relative orientation of chest radiographs would be very useful. Such a method would reduces the incidence of mislabeled or unlabeled images and save time used to reorient images. Thus, such a method would improve the efficiency and effectiveness of automated electronic image management and display.
Some work has been initiated to determine the orientation and view of chest radiographs.
Pieka et al. (“Orientation Correction for Chest Images”, Journal of Digital Imaging, Vol. 5, No. 3, 1992) presented an automatic method to determine the projection and orientation of chest images using two projection profiles of images, which are obtained by calculating the average densities along horizontal and vertical lines.
Boone et. al. (“Recognition of Chest Radiograph Orientation for Picture Archiving and Communication Systems Display Using Neural Networks”, Journal of Digital Imaging, Vol. 5, No. 3, 1992) used an artificial neural network to classify the orientation of chest radiographs. The features extracted include two projection profiles and four regions of interest. Evanoff et. Al. (“Automatically Determining the Orientation of Chest Images”, SPIE Vol 3035) applied linear regression on two orthogonal profiles to determine the top of the image, then sought the edge of heart to determine if the image requires reorientation. However, the two profiles mentioned in the above methods are very sensitive to noise in the images and the projection profile's features are not sufficiently detailed to allow differentiation of the PA and LAT projection views.
Arimura et al. (“Development of a computerized method for identifying the posteroanterior and lateral views of chest radiographs by use of a template matching technique”, Med. Phys. 29(7) July 2002 and U.S. Pat. Application No.2002/0021829 entitled METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR IDENTIFYING CHEST RADIOGRPAHS USING IMAGE MAPPING AND TEMPATE MATCHING TACHNIQUES) proposed a method to distinguish the PA or LAT projection views by examining the similarity of a chest image with pre-defined template images. However, the manual generation of the template images is quite time consuming, and more particularly, is highly observer dependent, which can introduce error into the classification.
Lehmann et al. (“Automatic Detection of the View Position of Chest Radiographs”, SPIE Proceeding, Vol. 5032) addressed Arimura's method by using only one template image and identifying the different views using the K-nearest-neighbor classifier. However, both template-matching methods suffer problems in handling rotation and translation variance of an individual patient's chest radiograph. In addition, the methods do not address the interference of noise or unrelated regions which can cause seriously biased classification results.
U.S. Pat. No. 5,862,249 issued Jan. 19, 1999 to Jang et al. entitled AUTOMATED METHOD AND SYSTEM FOR DETERMINATION OF POSITIONAL ORIENTATION OF DIGITAL RADIOGRAPHIC IMAGES is directed to a method for determining the orientation of images by means of multi-stage processing. The region of interest (ROI) is segmented from the chest image, and a set of rules is applied to determine the orientation of the image.
U.S. Pat. No. 6,055,326 issued Apr. 25, 2000 to Chang et al. entitled METHOD FOR ORIENTING ELECTRONIC MEDICAL IMAGES is directed to a method employing image segmentation and rules to determine the projection view type of a chest X-ray image and, based on the boundary of body parts, to determine orientation of the X-ray image. The method is disadvantaged for actual application since it is quite complicated; the rules are based only on the boundary information of the images, which is not reliable in most chest images because of variability of patient's position in the images and the existence of collimation areas in some images; and no actual image content information, such as lung regions, is considered in the method.
Accordingly, there is a need for a system and method for automatically recognizing the projection view and the patient-relative orientation of chest radiographs which overcomes the shortcomings of the prior art. Such a method should be a robust method to automatically identify the projection view and orientation of chest radiographs.